Physical marketplace interaction platform

ABSTRACT

Systems and techniques for a physical marketplace interaction platform are described herein. A user may be identified. An article may be identified based on a physical relationship with the user. A user activity with respect to the article may be determined based on an observed context of the user and the article, and the user activity may be reported.

TECHNICAL FIELD

Embodiments described herein generally relate to consumer interactionsin a physical marketplace and more specifically to a physicalmarketplace interaction platform.

BACKGROUND

A user (e.g., shopper, consumer, etc.) may walk through a physicalmarketplace (such as a retailer location, store, shopping mall, grocerystore, etc.) and interact (e.g., browse, carry, try on, sample, etc.)with articles (e.g., clothing, home goods, sporting goods, electronics,books, etc.) in the marketplace. In some examples, systems installed atthe physical marketplace track when the user enters or leaves thephysical market place, for example, using cameras. In some examples, thephysical marketplace may track user purchasing decisions at the point ofsale (POS).

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1 illustrates an example of an environment for a physicalmarketplace interaction platform, according to an embodiment.

FIG. 2 illustrates a block diagram of an example of a physicalmarketplace interaction platform, according to an embodiment.

FIG. 3 illustrates a block diagram of an example of a physicalmarketplace interaction platform, according to an embodiment.

FIG. 4 illustrates a flowchart of an example of a method to implement aphysical marketplace interaction platform, according to an embodiment.

FIG. 5 illustrates a flowchart of an example of a physical marketplaceinteraction platform operating under an example shopping scenario.

FIG. 6 is a block diagram illustrating an example of a machine uponwhich one or more embodiments may be implemented.

DETAILED DESCRIPTION

Current physical marketplace analysis of user article interactions isgenerally non-existent or limited to noting purchase decisions of users,for example at check-out, or user's entering or leaving the physicalmarketplace. Current physical marketplace observations, however, fail toprovide insight into how users interact with the articles of thephysical marketplace. In some examples, electronic marketplaces (e.g.,web sites from which goods may be purchased) attempt to track userinteractions with presented materials, such as viewing times, clicks,etc., however, these interactions are limited to user interactions withelements that may be displayed in an electronic form and theinteractions are likewise limited to those that may be measured fromelectronic inputs to the electronic marketplace.

The lack of information describing user interaction with articles in aphysical marketplace causes difficulties for operators to improve theuser's shopping experience. To address this issue, a physicalmarketplace interaction platform may be used to collect information ofuser interactions with articles in the physical marketplace. In anexample, the physical marketplace interaction platform may process thecollected information into actionable data. In an example, theactionable data may be used to improve the user's experience in thephysical marketplace. In an example, the actionable data may be used topromote sales in the physical marketplace, for example, by providingincentives to complete a sale, by featuring particular articles, etc.Thus, by observing user interactions with articles, the physicalmarketplace interaction platform may improve user experiences, and giveoperators greater options in productively running the physicalmarketplace.

For example, an article sensor (e.g., smart radio frequencyidentification (RFID) tag or other embedded devices) attached toclothing articles may connect with a shopper's smartphone when theshopper picks up the clothing article to look at it. Picking up theshirt may be the start of a “browsing event” between this shopper andthis specific clothing article that may be logged. The browsing eventmay include information, such as, the duration of the contact, and thedistance that the clothing article travels while connected with theshopper. The browsing event may be used to provide a framework for theretailer to offer real-time incentives to accelerate the shopper'spurchasing decision. For example, the platform may learn that a specificshopper spent eight minutes picking up several different colors of polojerseys, spending the most time with navy blue, black, and gray. Theshopper tried on a medium navy blue polo but not a black or gray polo.The shopper carried both the navy blue and gray polo jerseys through thestore but only purchased the gray polo. Knowing this behavior, anincentive for the shopper to purchase a second polo would have probablybeen successful.

As used herein, a “physical marketplace” is a physical location in whicharticles are made available for purchase by a user. Examples of physicalmarketplaces include stores, outlet centers, grocery stores, shoppingmalls, kiosks, service centers, physical markets (e.g., farmer's market,flee market, etc.), stalls, vehicles, such as a commercial airplanewhere a product purchase service is offered, etc. A physical marketplaceis “entered” at a point when a user may interact with articles of thephysical marketplace. Thus, in a traditional store, the physicalmarketplace is entered when a user steps inside the store. With respectto a kiosk or stall, the physical marketplace is entered when the useris close enough to observably interact with articles of the kiosk orstall. A physical marketplace is not a catalog, television/electronicservice (e.g., such as an infomercial with telephone number), telephonebased purchasing service, or electronic service (e.g., web site, mobilephone application, etc.). A retail area of the physical marketplace isan area in which articles are displayed. The retail area would notinclude, for example, a restroom, office, closet, etc. of a physicalmarketplace where those areas are not used for selling articles.

As used herein, an “article” is a product (e.g., clothes, toys, sportingequipment, office supplies, electronics, books, music, etc.) or aservice that is available for purchase at a physical marketplace. A useris able to interact with an article or a proxy for the article at thephysical marketplace. An article proxy may include such things as ademonstration model, a brochure (e.g., describing a vacation), a ticket(e.g., for an item too large to carry such as a large television,appliance, etc.), among other things. Either the article or the articleproxy is physically available in the physical marketplace.

As used herein, a “user” is an entity (e.g., a person) that may enterthe physical marketplace and effect a purchase of an article. In anexample, the entity may be proxy for a person, such as a telepresenceplatform permitting a remote person to interact with the physicalmarketplace or articles over a distance. In an example, the user is aguest to the physical marketplace. A guest is not an employee, operator(e.g., owner), or servicer (e.g., contractor working on behalf of theoperator), of the physical marketplace.

FIG. 1 illustrates an example of an environment 100 for a physicalmarketplace interaction platform. In this example of a physicalmarketplace, a user follows the path 105 after entering the physicalmarketplace. Along the path 105 are five points-of-interest (POI) 110 V,W, X, Y, and Z. Articles 115 A, B, C, D, and E are spread throughout thephysical marketplace. The physical marketplace includes a POS 130.

After entering the physical marketplace, the user may be identified.Identification may prevent tracking of uninteresting parties, such asemployees. In an example, identifying the user permits the physicalmarketplace interaction platform to record observed activity of the userfor future analytics. The user may be tracked to POI 110 V. A POI 110may be any pre-defined area in which a context of the user and article115 may be inferred. For example, POI 110 V may be a display of pants,POI 110 W may be a dressing room, POI 110 X may be a re-stocking area,POI 110 Y may be a checkout line, and POI 110 Z may be the checkout atthe POS 130. In an example, the POI 110 may refer to an area, such asthe room of the POI 110 V. In an example, a geometric area may bedefined, such as geometric area 120 corresponding to the POI 110 W andthe geometric area 125 corresponding to the POI 110 Y or the POI 110 Z.In the area around POI 110 V are four articles A, B, C, and D. Thus, itmay be inferred that the user is interacting with these articles 115based on the user's position at the POI 110 V. A further userinteraction with the articles 115 A and B may be inferred by theposition of the articles 115 and the user at the POI 110 W (a dressingroom). The observed context of proximity of the user and the articles115 A and B at the dressing room may denote a greater interest in thearticles 115 A and B for the user than for articles 115 C and D, towhich the user was exposed based on the user's proximity to the POI 110V. Another example observable context may be determined when, at the POI110 X (re-stocking) it is noted that article 115 B is no longer with theuser while article 115 A remains with the user. Thus, an inference thatan attribute of article 115 A is more important to the user than someattribute of article 115 B may be made. The POI 110 Y is the checkoutline. The duration of the user's stay in the checkout line may bemeasured. In an example, the longer the duration, the greater weight maybe given to the article 115 A's attributes to indicate desire on thepart of the user. The position of the article 115 A and the user at thePOI 110 Z (checkout) allows an inference that the user intends topurchase the article 115.

Any mechanism by which the user and articles 115 are tracked may be usedto implement the POI 110 mechanism described above. In an example, alocation based service (LBS) of the physical marketplace may be used todetermine the user's position or the position of any article 115. Otherexample location technologies may include satellite positioning systems(e.g., the Global Positing system), ground based radio systems (e.g.,cellular telephone trilateration), chemical sensors, etc.

The proximity of the user at POI 110 V to articles 115 A and B providesa type of observed context between the user and these articles 115 inwhich user activity may be measured. Other example observed contexts mayinclude manipulation of the article 115 by the user. For example, asmart RFID may provide proximity information to, for example, a mobiledevice of the user via its RFID functionality while also including anaccelerometer to determine if the article 115 is picked up. A smart RFIDis an example of an article sensor. An article sensor is any sensor inobservable contact with a specific article 115 and capable of providinginformation of an interaction context. Example observable sensors mayinclude, a scale (upon which the article rests), an eye tracker (todetermine whether the user is looking at the article), a touch sensor(such as a capacitive arrangement on a conductive article to sense humantouch), among others.

FIG. 2 illustrates a block diagram of an example of a physicalmarketplace interaction platform 200. The physical marketplaceinteraction platform 200 allows for use of observable contextinformation of physical interactions between users and articles, such asthat described above, to enhance the user experience at the physicalmarketplace as well as provide tools for an operator of the physicalmarketplace to increase sales. The physical marketplace interactionplatform 200 may include a user module 205, a report module 210, and anactivity module 215. These modules may be colocated on a single machineor separated from each other and be communicatively coupled when inoperation (e.g., via a network). In an example, one or more of thedescribed components may operate in a cloud (e.g., of the physicalmarketplace) in order to provide computational flexibility.

The user module 205 may be used to identify a user. In an example,identifying the user includes using a mobile device of the user. Forexample, the user's mobile device may include an application to connectto the physical marketplace interaction platform 200 (e.g., the usermodule 205) and provide identification information, such as a user name,member ID, etc. In an example, identifying the user includes creating ananonymized identification of the user. Such an anonymized identificationmay include such information as an anonymous ID, demographic information(e.g., age, sex, job, etc.), time of day, day of month, etc. In anexample, the anonymized identification is specific with respect toactions of the user and general with respect to a general identity ofthe user. For example, that the user picked up a particular platepattern, carried a card of the set (e.g., an article proxy) to theregister, and did not purchase the plates may be specifically attributedto the demographic information of the user, but not to the user himself.

In an example, identifying the user may include identifying a companionof the user. In this example, the observed context may include thecompanion. For example, it may be observed that a user is near anotheruser. This proximity may be observed in different visits to the physicalmarketplace, or over an extended period during a single visit. Suchcompanion identification may provide additional data to the observedcontext. For example, a pair shopping for flatware may allow theinference that they live together and suggest other items that may be ofinterest to them.

The activity module 215 may be used to identify an article based on aphysical relationship with the user. In an example, the physicalrelationship may include a distance between the article and the user.For example, if the user is within RFID range of the article, thearticle may be identified. In another example, if the user is at adisplay, articles in the display may be identified. In an example, thephysical relationship may include the presence of the user and thearticle within a predetermined geometric shape, such as geometric shape120 or 125 described above. Use of the geometric shape may solve someinteraction ambiguities. For example, a user may take several articlesto a dressing room but only allowed one at a time in a stall. That thearticles may not be next to the user (e.g., out of RFID range) isinconsequential until either the user or the article leaves the area.This may also be true for checkout lines if, for example, a belt, valet,or other mechanism exists to convey the article to the POS outside ofthe user's possession. In an example, the predetermined geometric shapemay be one of a plurality of geometric shapes defined for a retail areaincluding the article.

The activity module 215 may also be used to determine a user activitywith respect to the user and the article based on an observed context ofthe user and the article. An observed context includes informationobtainable by the activity module about the user and the article. Forexample, the observed context may include article position information.In an example, the position information may include a geospatialposition relative to a retail area including the article. In an example,the user activity may include physical possession of the article by theuser and at least one of duration of possession or location ofpossession.

In an example, the position information may include an articlearrangement. In an example, the article arrangement may include anorientation with respect to the user. For example, a flat article may bepicked up and tilted into a more vertical orientation (e.g., so that theuser may see it better). In an example, article arrangement may include,such things as an unfolding of the article, repositioning the article ona stand, etc. As noted here and above, article sensors or positioningsystems may be used, among other things, to provide observed contextinformation. In an example, a mobile device operated by the user may beused to identify the article (e.g., via RFID, scanning a bar code,etc.). In an example, the mobile device may be used to determine theuser activity. For example, a smart RFID tag on a tool may communicate,via the mobile phone, that the tool is moving (e.g., via anaccelerometer). This information may be passed to the activity module215 via the mobile device.

In an example, the activity module 215 may include, or may interfacewith, an article tracking module 225, a POI ID module 230, and acorrelation module 235 to determine the user activity. The articletracking module 225 may track the article in a retail area. For example,the article may include a passive RFID tag. A series of RFIDinterrogators in a department may periodically interrogate RFID tags. Bynoting which interrogators may read a particular RFID tag, and knowingthe positing of the interrogators within the retail area, as position ofthe article may be ascertained. In an example, an article sensor mayemploy a location service (e.g., GPS, LBS, etc.) and report its positionto the article tracking module 225. In an example, a camera system mayprovide still or video images that may be processed to identify thearticle. Knowing the retail area positions any given view coversprovides a location of the article.

The user tracking module 220 may track the user in the retail area. Theuser tracking module 220 is distinguishable from the user module 205 inthat the first determines user position within the physical marketplacewhile the second is concerned with personal information about the user.The user tracking module 220 may employ any of the techniques describedabove with respect to article tracking. In an example, the mobile deviceof the user is employed to aid in the user tracking. In an example, themobile device provides the user's position.

The POI ID module 230 may identify a POI in the retail area. Forexample, the POI IS module 230 may provide an interface in which a POIis defined. Such an interface may provide for the identification of anarea (e.g., sporting goods) or a geometric shape.

The correlation module 235 may note a confluence of the article, theuser, and the POI. For example, the user and the article may be trackedseparately by the article tracking module 225 and the user trackingmodule 220 respectively. Observing that the article and user movedtogether into a POI may allow the inference that the user is inpossession of the article. This may still, however, not be sufficient todetermine other ways in which the user is interacting with the article.In this example, the POI may be used to provide additional interactioninformation. For example, if the user is observed with the article at adressing room and also at checkout, it may be inferred that the usertried on the article and is willing to buy the article.

The report module 210 may report on the user activity. In an example,reporting the user activity may include adding an event to a browsinghistory for the user. In an example, the browsing history may bemaintained by the user module 205. The browsing history may include useractivity data indexed by visits of the user to the physical marketplace.In an example, the browsing history may include information about uservisits to other physical marketplaces. In an example, the browsinghistory may include a correlation model to identify other users similarto the user. In an example, these other user's browsing history may beused to supplement the user's browsing history.

In an example, reporting the user activity may include communicating theactivity to a market research platform 240. In an example, reporting tothe market research platform 240 may include anonymizing the user. Suchcommunications may provide an additional revenue stream for the physicalmarket place. In an example, the market research platform 240 mayaggregate user activity data for a particular article or class ofarticles. In an example, the market research platform 240 maycommunicate activity information to third parties, such as articlemanufacturers, other retailers, etc.

In an example, reporting the user activity may include communicating theactivity to an incentives platform 245. In an example, a delivery module250 may be used to deliver a purchase incentive to the user from theincentives platform. In an example, the purchase incentive may bedelivered to a mobile device of the user.

Example purchase incentives may include a discount on the article,notification of a complimentary article (e.g., jewelry to match shirt,knife block to match knife, etc.), coupon on unrelated service (e.g.,free car wash at another retailer for purchasing vacation), etc. In anexample, the purchase incentive may include representations of otherarticles of interest to the user based on a browsing history of theuser. For example, a user who has bought jeans in the past may bepresented with an advertisement for a new brand of jeans being offeredat the physical marketplace. In an example, the purchase incentive mayinclude a map of the retail area indicating locations for the otherarticles, such as the jeans. In another example, a user observedinteracting with a shirt may be presented other articles to complete anoutfit. The map would indicate the locations of the other articlescomprising the ensemble.

In an example, the purchase incentive may be selected from a pluralityof purchase incentives based on an identified companion of the user. Forexample, a user with a partner identified as a companion near a holidaymay be offered a romantic article in the purchase incentive. In anotherexample, a user with a companion identified as a child may be offered apurchase incentive for a toy, for example, near the companion'sbirthday.

In an example, the purchase incentive may be selected based on apredictive analytic. A predictive analytic is a model designed topredict future behavior based on current behavior. Examples may includemodels based on age, socio-economic status, taste profiles, etc. In anexample, the predictive analytic may be derived from a browsing historyof the user.

FIG. 3 illustrates a block diagram of an example of a physicalmarketplace interaction platform 300. The platform 300 includes avariation on task separation from that discussed above with respect tothe platform 200. The platform 300 generally separates tasks along abrowsing event. A browsing event is a period and corresponding activitybetween a user and an article. For example, a browsing event may beginwhen the user looks at an article and end when the user puts the articledown or purchases the article. In an example, browsing events may besubdivided into discrete browsing actions. For example, a user pickingup an article may be a first browsing action and the user turning theshirt over may be a second browsing action. The platform 300 may includea browsing event trigger module 305, a browsing event tracking module310, a browsing event storage module 320, a browsing event comparisonmodule 315, and a browsing learning and incentive module 325.

The browsing event trigger module 305 may interface between articlesensors and a user's mobile device to determine whether the user isinteracting with the article. Once it is determined that an interactionis taking place, the browsing event trigger module 305 initiates abrowsing event.

The browsing event tracking module 310 may track data of the browsingevent. In an example, the tracked data may include a duration of thebrowsing event. In an example, the tracked data may include a distancethat the user travels with the article. In an example, geospatialtracking information from an LBS of the physical marketplace may be partof the tracked data.

The browsing storage module 320 may store browsing information. In anexample, the browsing information may include one or more of, the user,the article that was browsed, purchase tendencies of the user,relationship between browsing events and purchase decisions, etc. Thebrowsing storage module 320 acts as a data repository from whenceadditional browsing analytics may be derived and stored for future use.

The browsing event comparison module 315 may compare an on-going (e.g.,real-time) browsing event with historic characteristics (e.g.,behaviors) of users (e.g., the current user of the on-going browsingevent) to determine a predictive analytic for the browsing event. Thus,the browsing event comparison module 315 may bridge the gap betweenhistorical knowledge and current activities to identify a model for thecurrent activity.

The browsing learning and incentive module 325 may use the determinedpredictive analytic from the browsing event comparison module 315 toenact an incentive designed to accelerate the user's decision topurchase an article. For example, the browsing learning and incentivemodule 325 may determine—e.g., based on the data mining of shopper'sprevious physical store browsing characteristics, and purchaseresults—whether the best timing for an incentive for this specificshopper is when they first pick up the clothing article or after theytry it on in the changing room. In an example, the browsing learning andincentive module 325 may determine that the incentive worked in anunexpected manner (e.g., was more successful or less successful thanpredicted) and modify the predictive analytic to account for thevariance. For example, an incentive that suggested an add-on item of atie when a sport coat is being browsed, may be changed when suggestingthe tie rarely results in a sale (e.g., when fashions change).

The following is an example of a scenario using the browsing eventcentric arrangement described above with respect to the physicalmarketplace interaction platform 300. Chris is shopping in the localclothing store, looking to buy a summer jersey. He is looking at thedisplay of summer jerseys. He scans the various jerseys on the displaytable, and spots one that he likes. He picks it up off of the displaytable. The article sensor in the jersey detects that the jersey has beenpicked up off of the display table and connects with Chris' smartphoneto start a browsing event. This is transmitted via the smartphone to thephysical marketplace interaction platform 300 resident in the clothingstore's private cloud where Chris's physical store browsing and purchasehistory are accessed.

The physical marketplace interaction platform 300 may measure theduration that Chris holds onto the jersey, as well as whether Chrismoves the jersey from the table and carries it to another location inthe store to a location, such as a dressing (e.g., changing) room. Usinga predictive analytic, the physical marketplace interaction platform 300determines Chris' affinity (e.g., buying desire) for the jersey by theduration of time that Chris holds the jersey and if Chris takes thejersey away from the display table. The physical marketplace interactionplatform 300 sends (via text message, etc.) a real time incentive toChris to buy the jersey in his hand now. Based on Chris' historicalbrowsing and purchasing data at the clothing store, the physicalmarketplace interaction platform 300 determines that once Chris tries ona piece of clothing, or otherwise holds on to the clothing article forlonger than two minutes and ten seconds, he will make a purchase with87% probability. Therefore the physical marketplace interaction platform300 offers him a 15% coupon incentive when he picks up the jersey andholds it for longer than 20 seconds.

In a further example of a scenario using the browsing event centricarrangement described above with respect to the physical marketplaceinteraction platform 300, Erin is shopping at the clothing store and islooking for a new pair of jeans. She takes a pair of jeans that she isinterested in from the rack. As in the example with Chris, the physicalmarketplace interaction platform 300 logs a browsing event. The physicalmarketplace interaction platform 300 uses Erin's physical store browsingand purchasing records. The physical marketplace interaction platform300 determines that she has previously looked at (e.g., triggeredbrowsing events) several shirts and other tops. The physical marketplaceinteraction platform 300 texts Erin a real-time offer for a 30% discountif she buys the jeans that she is looking at, and a matching shirt andsocks which she has looked at in a previous visit.

FIG. 4 illustrates a flowchart of an example of a method 400 toimplement a physical marketplace interaction platform.

At operation 405, a user may be identified. In an example, identifyingthe user may include using a mobile device operated by the user. In anexample, identifying the user may include creating an anonymizedidentification for the user. In an example the anonymized identificationis specific with respect to actions of the user and general with respectto a general identify of the user. In an example, identifying the usermay include identifying a companion of the user.

At operation 410, an article may be identified based on a physicalrelationship with the user. In an example, the physical relationship mayinclude the distance between the article and the user. In an example,the physical relationship may include inclusion of both the user and thearticle within a predetermined geometric shape. In an example, thepredetermined geometric shape may be one of a plurality of geometricshapes defined for a retail area including the article.

At operation 415, a user activity with respect to the user and thearticle may be determined based on an observed context of the user andthe article. In an example, the observed context may include articleposition information. In an example, the article position informationmay include a geospatial position relative to a retail area includingthe article. In an example, the article position information may includean article arrangement. In an example, the article arrangement mayinclude an orientation with respect to the user. In an example, theobserved context may include the companion. In an example, the useractivity may include physical possession of the article by the user. Inan example, the user activity may include a duration of possession. Inan example, the user activity may include a location of possession. Inan example, the user activity may include a manipulation (e.g., holding,touching, wearing etc.) of the article by the user.

In an example, identifying the article and determining the user activitymay be performed by a mobile device operated by the user. For example,the mobile device may identify the article via near field communicationswhen the user touches the article with the mobile device. Further, themobile device may report a picture of the article taken by the user and,for example, sent to a companion (e.g., a spouse).

In an example, determining the user activity may include tracking thearticle in a retail area, tracking the user in the retail area,identifying a POI in the retail area, and noting a confluence of thearticle, the user, and the POI.

At operation 420, the user activity may be reported. In an example,reporting the user activity may include adding an event to a browsinghistory for the user. In an example, reporting the user activityincludes communicating the activity to a market research platform. In anexample, reporting the user activity to the market research platform mayinclude anonymizing the user.

In an example, reporting the user activity may include communicating theactivity to an incentives platform.

In an example, the method 400 may comprise delivering a purchaseincentive to the user from the incentives platform. In an example, thepurchase incentive may be delivered to a mobile device of the user. Inan example, the purchase incentive may include representations of otherarticles of interest to the user based on a browsing history of theuser. In an example, the purchase incentive may include a map of aretail area. The map may indicate locations for the other articles.

In an example, the purchase incentive may be selected from a pluralityof purchase incentives based on the companion. In an example, thepurchase incentive may be selected based a predictive analytic. In anexample, the predictive analytic may be derived from a browsing historyof the user.

FIG. 5 illustrates a flowchart of an example of a physical marketplaceinteraction platform operating under an example shopping scenario 500.In the scenario 500, a user interacts with an article which is a shirt.The scenario 500 may be implemented with any of the systems or methodsdiscussed above.

At point 505, is a starting condition of a shopper browsing at aphysical retailer, such as a clothier.

At point 510, the shopper picks up a shirt.

At point 515, an article sensor, such as an embedded tag in the shirt,connects with the shopper's mobile device.

At point 520, the physical marketplace interaction platform (e.g.,retailer platform) logs the start of the browsing event. In an example,the browsing event information is communicated to physical marketplaceinteraction platform from the mobile device. At this juncture, thephysical marketplace interaction platform both processes browsinganalytics for the browsing event (points 525-535 below) as well ascontinues to collect browsing event information (points 555-565 below).

At point 525, the physical marketplace interaction platform retrievesthe shopper's historic shopping characteristics, such as browsing orpurchasing characteristics. In an example, these characteristics may bespecific to any of location, product, season, etc. In an example, thesecharacteristics may be generalized. For example, the characteristic maybe specific to the season but not specific to the location or product.

At point 530, the physical marketplace interaction platform may comparethe historic shopping characteristics (e.g., historic data) to on-goingbrowsing event data (e.g., real-time actuals of the browsing event). Asillustrated by the dashed lines, this on-going browsing event data maybe collected from a tracking processes running in parallel to thisprocess. Comparing the on-going data with the historical data may permita model of the current shopping experience to be identified. Forexample, different behavioral models may have been developed based onprevious shopping trips that are recorded in the historic data. Byidentifying similarities between an on-going browsing event and ahistoric browsing event, a model corresponding to the historic browsingevent may be relevant to the on-going browsing event. Thus, the historicdata may be leveraged to provide greater insight into an on-going userinteraction for the physical marketplace.

At decision point 535, a decision may be made—e.g., based on the model,historic data, or on-going browsing behavior—as to whether a targetedincentive will accelerate a purchasing decision. If it is determinedthat a targeted incentive will not move the shopper, the physicalmarketplace interaction platform proceeds back to the initial conditionof point 505.

At point 540, if the determination from point 535 is that a targetedincentive is warranted, the physical marketplace interaction platformdetermines what that incentive is. In an example, the model, personalinformation about the user, or other factors may form the basis for theincentive.

At point 545, the physical marketplace interaction platform communicatesthe incentive to the user. In an example, the communication takes theform of a text message to the user's mobile device.

At decision point 550, it is determined whether the shopper accepts thetargeted incentive. If the determination is no, the shopper did notaccept the targeted incentive, the physical marketplace interactionplatform proceeds back to point 530 to determine whether anotherincentive may work. If the determination is yes, the shopper did acceptthe targeted incentive, a browsing update may be recorded (e.g., atpoint 525) and the physical marketplace interaction platform proceedsback to the initial condition of point 505.

At point 555, an in-store LBS may track the location (e.g., within thestore) of the combined shirt and mobile device. In an example, aphysical relationship to a POI may also be tracked. For example, thelocation of the shirt may be tracked relative to the browsing event'sorigin (e.g., where the shirt was picked up) or the dressing room.

At decision point 560, a determination may be made as to whether theshopper is stationary with the shirt. If yes, the tracking of point 555may continue. If no, the physical marketplace interaction platform mayproceed to decision point 565. In either case, from decision point 560,555, or, 565, tracking information may be communicated to point 530continuously, continually, or otherwise, via the data feed.

At decision point 565, a determination may be made as to whether theshopper is in the dressing room. As a POI, the dressing room may signifya great interest in the product by the shopper. If it is determined thatthe shopper is not in the dressing room, the physical marketplaceinteraction platform may proceed to point 555 to continue tracking thebrowsing event. If it is determined that the shopper is in the dressingroom, the physical marketplace interaction platform may proceed to point540 (or 530) to determine whether it may accelerate the purchasingdecision.

FIG. 6 illustrates a block diagram of an example machine 600 upon whichany one or more of the techniques (e.g., methodologies) discussed hereinmay perform. In alternative embodiments, the machine 600 may operate asa standalone device or may be connected (e.g., networked) to othermachines. In a networked deployment, the machine 600 may operate in thecapacity of a server machine, a client machine, or both in server-clientnetwork environments. In an example, the machine 600 may act as a peermachine in peer-to-peer (P2P) (or other distributed) networkenvironment. The machine 600 may be a personal computer (PC), a tabletPC, a set-top box (STB), a personal digital assistant (PDA), a mobiletelephone, a web appliance, a network router, switch or bridge, or anymachine capable of executing instructions (sequential or otherwise) thatspecify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein, such as cloud computing, software asa service (SaaS), other computer cluster configurations.

Examples, as described herein, may include, or may operate on, logic ora number of components, modules, or mechanisms. Modules are tangibleentities (e.g., hardware) capable of performing specified operationswhen operating. A module includes hardware. In an example, the hardwaremay be specifically configured to carry out a specific operation (e.g.,hardwired). In an example, the hardware may include configurableexecution units (e.g., transistors, circuits, etc.) and a computerreadable medium containing instructions, where the instructionsconfigure the execution units to carry out a specific operation when inoperation. The configuring may occur under the direction of theexecutions units or a loading mechanism. Accordingly, the executionunits are communicatively coupled to the computer readable medium whenthe device is operating. In this example, the execution units may be amember of more than one module. For example, under operation, theexecution units may be configured by a first set of instructions toimplement a first module at one point in time and reconfigured by asecond set of instructions to implement a second module.

Machine (e.g., computer system) 600 may include a hardware processor 602(e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 604 and a static memory 606, some or all of which may communicatewith each other via an interlink (e.g., bus) 608. The machine 600 mayfurther include a display unit 610, an alphanumeric input device 612(e.g., a keyboard), and a user interface (UI) navigation device 614(e.g., a mouse). In an example, the display unit 610, input device 612and UI navigation device 614 may be a touch screen display. The machine600 may additionally include a storage device (e.g., drive unit) 616, asignal generation device 618 (e.g., a speaker), a network interfacedevice 620, and one or more sensors 621, such as a global positioningsystem (GPS) sensor, compass, accelerometer, or other sensor. Themachine 600 may include an output controller 628, such as a serial(e.g., universal serial bus (USB), parallel, or other wired or wireless(e.g., infrared (IR), near field communication (NFC), etc.) connectionto communicate or control one or more peripheral devices (e.g., aprinter, card reader, etc.).

The storage device 616 may include a machine readable medium 622 onwhich is stored one or more sets of data structures or instructions 624(e.g., software) embodying or utilized by any one or more of thetechniques or functions described herein. The instructions 624 may alsoreside, completely or at least partially, within the main memory 604,within static memory 606, or within the hardware processor 602 duringexecution thereof by the machine 600. In an example, one or anycombination of the hardware processor 602, the main memory 604, thestatic memory 606, or the storage device 616 may constitute machinereadable media.

While the machine readable medium 622 is illustrated as a single medium,the term “machine readable medium” may include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 624.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 600 and that cause the machine 600 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples mayinclude solid-state memories, and optical and magnetic media. In anexample, a massed machine readable medium comprises a machine readablemedium with a plurality of particles having resting mass. Specificexamples of massed machine readable media may include: non-volatilememory, such as semiconductor memory devices (e.g., ElectricallyProgrammable Read-Only Memory (EPROM), Electrically ErasableProgrammable Read-Only Memory (EEPROM)) and flash memory devices;magnetic disks, such as internal hard disks and removable disks;magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 624 may further be transmitted or received over acommunications network 626 using a transmission medium via the networkinterface device 620 utilizing any one of a number of transfer protocols(e.g., frame relay, internet protocol (IP), transmission controlprotocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 620 may include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas to connect tothe communications network 626. In an example, the network interfacedevice 620 may include a plurality of antennas to wirelessly communicateusing at least one of single-input multiple-output (SIMO),multiple-input multiple-output (MIMO), or multiple-input single-output(MISO) techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 600, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software.

Additional Notes & Examples

Example 1 includes subject matter (such as a device, apparatus, or asystem for monitoring physical interactions with merchandise)comprising: a user module to identify a user; an activity module to:identify an article based on a physical relationship with the user; anddetermine a user activity with respect to the user and the article basedon an observed context of the user and the article; and a report moduleto report on the user activity.

In Example 2, the subject matter of Example 1 may optionally include,wherein to identify the user includes using a mobile device operated bythe user.

In Example 3, the subject matter of any one of Examples 1-2 mayoptionally include. The system of claim C1, wherein to identify the userincludes creating an anonymized identification for the user, theanonymized identification specific with respect to actions of the userand general with respect to a general identify of the user.

In Example 4, the subject matter of any one of Examples 1-3 mayoptionally include, wherein the physical relationship includes distancebetween the article and the user.

In Example 5, the subject matter of any one of Examples 1-4 mayoptionally include, wherein the physical relationship includes inclusionof both the user and the article within a predetermined geometric shape,the predetermined geometric shape being one of a plurality of geometricshapes defined for a retail area including the article.

In Example 6, the subject matter of any one of Examples 1-5 mayoptionally include, wherein the observed context includes articleposition information.

In Example 7, the subject matter of any one of Examples 1-6 mayoptionally include, wherein the position information includes ageospatial position relative to a retail area including the article.

In Example 8, the subject matter of any one of Examples 1-7 mayoptionally include, wherein the position information includes an articlearrangement.

In Example 9, the subject matter of any one of Examples 1-8 mayoptionally include, wherein the article arrangement includes anorientation with respect to the user.

In Example 10, the subject matter of any one of Examples 1-9 mayoptionally include, wherein a mobile device operated by the user is usedto both identify the article and determine the user activity.

In Example 11, the subject matter of any one of Examples 1-10 mayoptionally include, wherein to determine the user activity includes: anarticle tracking module to track the article in a retail area; a usertracking module to track the user in the retail area; point-of-interestidentification module to identifying a point of interest in the retailarea; and correlation module to note a confluence of the article, theuser, and the point of interest.

In Example 12, the subject matter of any one of Examples 1-11 mayoptionally include, wherein to report the user activity includes addingan event to a browsing history for the user.

In Example 13, the subject matter of any one of Examples 1-12 mayoptionally include, wherein to report the user activity includescommunicating the activity to a market research platform.

In Example 14, the subject matter of any one of Examples 1-13 mayoptionally include, wherein to report the user activity to the marketresearch platform includes anonymizing the user.

In Example 15, the subject matter of any one of Examples 1-14 mayoptionally include, wherein to report the user activity includescommunicating the activity to an incentives platform.

In Example 16, the subject matter of any one of Examples 1-15 mayoptionally include a delivery module to deliver a purchase incentive tothe user from the incentives platform.

In Example 17, the subject matter of any one of Examples 1-16 mayoptionally include, wherein the purchase incentive is delivered to amobile device of the user.

In Example 18, the subject matter of any one of Examples 1-17 mayoptionally include, wherein the purchase incentive includesrepresentations of other articles of interest to the user based on abrowsing history of the user.

In Example 19, the subject matter of any one of Examples 1-18 mayoptionally include, wherein the purchase incentive includes a map of aretail area, the map indicating locations for the other articles.

In Example 20, the subject matter of any one of Examples 1-19 mayoptionally include, wherein to identify the user includes identifying acompanion of the user, wherein the observed context includes thecompanion, and wherein the purchase incentive is selected from aplurality of purchase incentives based on the companion.

In Example 21, the subject matter of any one of Examples 1-20 mayoptionally include, wherein the purchase incentive is selected based apredictive analytic, the predictive analytic derived from a browsinghistory of the user.

In Example 22, the subject matter of any one of Examples 1-21 mayoptionally include, wherein the user activity includes physicalpossession of the article by the user and at least one of duration ofpossession or location of possession.

Example 23 may include, or may optionally be combined with the subjectmatter of any one of Examples 1-22 to include, subject matter (such as amethod, means for performing acts, or machine readable medium includinginstructions that, when performed by a machine cause the machine toperforms acts) for monitoring physical interactions with merchandise,comprising: identifying a user; identifying an article based on aphysical relationship with the user; determining a user activity withrespect to the user and the article based on an observed context of theuser and the article; and reporting the user activity.

In Example 24, the subject matter of Example 23 may optionally include,wherein identifying the user includes using a mobile device operated bythe user.

In Example 25, the subject matter of any one of Examples 23-24 mayoptionally include, wherein identifying the user includes creating ananonymized identification for the user, the anonymized identificationspecific with respect to actions of the user and general with respect toa general identify of the user.

In Example 26, the subject matter of any one of Examples 23-25 mayoptionally include, wherein the physical relationship includes distancebetween the article and the user.

In Example 27, the subject matter of any one of Examples 23-26 mayoptionally include, wherein the physical relationship includes inclusionof both the user and the article within a predetermined geometric shape,the predetermined geometric shape being one of a plurality of geometricshapes defined for a retail area including the article.

In Example 28, the subject matter of any one of Examples 23-27 mayoptionally include, wherein the observed context includes articleposition information.

In Example 29, the subject matter of any one of Examples 23-28 mayoptionally include, wherein the position information includes ageospatial position relative to a retail area including the article.

In Example 30, the subject matter of any one of Examples 23-29 mayoptionally include, wherein the position information includes an articlearrangement.

In Example 31, the subject matter of any one of Examples 23-30 mayoptionally include, wherein the article arrangement includes anorientation with respect to the user.

In Example 32, the subject matter of any one of Examples 23-31 mayoptionally include, wherein both identifying the article and determiningthe user activity are performed by a mobile device operated by the user.

In Example 33, the subject matter of any one of Examples 23-32 mayoptionally include, wherein determining the user activity includes:tracking the article in a retail area; tracking the user in the retailarea; identifying a point of interest in the retail area; and noting aconfluence of the article, the user, and the point of interest.

In Example 34, the subject matter of any one of Examples 23-33 mayoptionally include, wherein reporting the user activity includes addingan event to a browsing history for the user.

In Example 35, the subject matter of any one of Examples 23-34 mayoptionally include, wherein reporting the user activity includescommunicating the activity to a market research platform.

In Example 36, the subject matter of any one of Examples 23-35 mayoptionally include, wherein reporting the user activity to the marketresearch platform includes anonymizing the user.

In Example 37, the subject matter of any one of Examples 23-36 mayoptionally include, wherein reporting the user activity includescommunicating the activity to an incentives platform.

In Example 38, the subject matter of any one of Examples 23-37 mayoptionally include, comprising delivering a purchase incentive to theuser from the incentives platform.

In Example 39, the subject matter of any one of Examples 23-38 mayoptionally include, wherein the purchase incentive is delivered to amobile device of the user.

In Example 40, the subject matter of any one of Examples 23-39 mayoptionally include, wherein the purchase incentive includesrepresentations of other articles of interest to the user based on abrowsing history of the user.

In Example 41, the subject matter of any one of Examples 23-40 mayoptionally include, wherein the purchase incentive includes a map of aretail area, the map indicating locations for the other articles.

In Example 42, the subject matter of any one of Examples 23-41 mayoptionally include, wherein identifying the user includes identifying acompanion of the user, wherein the observed context includes thecompanion, and wherein the purchase incentive is selected from aplurality of purchase incentives based on the companion.

In Example 43, the subject matter of any one of Examples 23-42 mayoptionally include, wherein the purchase incentive is selected based apredictive analytic, the predictive analytic derived from a browsinghistory of the user.

In Example 44, the subject matter of any one of Examples 23-43 mayoptionally include, wherein the user activity includes physicalpossession of the article by the user and at least one of duration ofpossession or location of possession.

Example 45 includes subject matter, or may optionally be combined withany of Examples 1-44 to include subject matter, such as a system formonitoring physical interactions with merchandise, the systemcomprising: user identification means to identify a user; articleidentification means to identify an article based on a physicalrelationship with the user; activity determination means to determine auser activity with respect to the user and the article based on anobserved context of the user and the article; and report means to reporton the user activity.

In Example 46, the subject matter of Example 45 may optionally include,wherein to identify the user includes using a mobile device operated bythe user.

In Example 47, the subject matter of any one of Examples 45-46 mayoptionally include, wherein to identify the user includes creating ananonymized identification for the user, the anonymized identificationspecific with respect to actions of the user and general with respect toa general identify of the user.

In Example 48, the subject matter of any one of Examples 45-47 mayoptionally include, wherein the physical relationship includes distancebetween the article and the user.

In Example 49, the subject matter of any one of Examples 45-48 mayoptionally include, wherein the physical relationship includes inclusionof both the user and the article within a predetermined geometric shape,the predetermined geometric shape being one of a plurality of geometricshapes defined for a retail area including the article.

In Example 50, the subject matter of any one of Examples 45-49 mayoptionally include, wherein the observed context includes articleposition information.

In Example 51, the subject matter of any one of Examples 45-50 mayoptionally include, wherein the position information includes ageospatial position relative to a retail area including the article.

In Example 52, the subject matter of any one of Examples 45-51 mayoptionally include, wherein the position information includes an articlearrangement.

In Example 53, the subject matter of any one of Examples 45-52 mayoptionally include, wherein the article arrangement includes anorientation with respect to the user.

In Example 54, the subject matter of any one of Examples 45-53 mayoptionally include, wherein a mobile device operated by the user is usedto both identify the article and determine the user activity.

In Example 55, the subject matter of any one of Examples 45-54 mayoptionally include, wherein to determine the user activity includes:article tracking means to track the article in a retail area; usertracking means to track the user in the retail area; point-of-interestidentification means to identifying a point of interest in the retailarea; and correlation means to note a confluence of the article, theuser, and the point of interest.

In Example 56, the subject matter of any one of Examples 45-55 mayoptionally include, wherein to report the user activity includes addingan event to a browsing history for the user.

In Example 57, the subject matter of any one of Examples 45-56 mayoptionally include, wherein to report the user activity includescommunicating the activity to a market research platform.

In Example 58, the subject matter of any one of Examples 45-57 mayoptionally include, wherein to report the user activity to the marketresearch platform includes anonymizing the user.

In Example 59, the subject matter of any one of Examples 45-58 mayoptionally include, wherein to report the user activity includescommunicating the activity to an incentives platform.

In Example 60, the subject matter of any one of Examples 45-59 mayoptionally include, delivery means to deliver a purchase incentive tothe user from the incentives platform.

In Example 61, the subject matter of any one of Examples 45-60 mayoptionally include, wherein the purchase incentive is delivered to amobile device of the user.

In Example 62, the subject matter of any one of Examples 45-61 mayoptionally include, wherein the purchase incentive includesrepresentations of other articles of interest to the user based on abrowsing history of the user.

In Example 63, the subject matter of any one of Examples 45-62 mayoptionally include, wherein the purchase incentive includes a map of aretail area, the map indicating locations for the other articles.

In Example 64, the subject matter of any one of Examples 45-63 mayoptionally include, wherein to identify the user includes identifying acompanion of the user, wherein the observed context includes thecompanion, and wherein the purchase incentive is selected from aplurality of purchase incentives based on the companion.

In Example 65, the subject matter of any one of Examples 45-64 mayoptionally include, wherein the purchase incentive is selected based apredictive analytic, the predictive analytic derived from a browsinghistory of the user.

In Example 66, the subject matter of any one of Examples 45-65 mayoptionally include, wherein the user activity includes physicalpossession of the article by the user and at least one of duration ofpossession or location of possession.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments that may bepracticed. These embodiments are also referred to herein as “examples.”Such examples may include elements in addition to those shown ordescribed. However, the present inventors also contemplate examples inwhich only those elements shown or described are provided. Moreover, thepresent inventors also contemplate examples using any combination orpermutation of those elements shown or described (or one or more aspectsthereof), either with respect to a particular example (or one or moreaspects thereof), or with respect to other examples (or one or moreaspects thereof) shown or described herein.

All publications, patents, and patent documents referred to in thisdocument are incorporated by reference herein in their entirety, asthough individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended, that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim are still deemed to fall within thescope of that claim. Moreover, in the following claims, the terms“first,” “second,” and “third,” etc. are used merely as labels, and arenot intended to impose numerical requirements on their objects.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments may be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is to allow thereader to quickly ascertain the nature of the technical disclosure, forexample, to comply with 37 C.F.R. §1.72(b) in the United States ofAmerica. It is submitted with the understanding that it will not be usedto interpret or limit the scope or meaning of the claims. Also, in theabove Detailed Description, various features may be grouped together tostreamline the disclosure. This should not be interpreted as intendingthat an unclaimed disclosed feature is essential to any claim. Rather,inventive subject matter may lie in less than all features of aparticular disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment. The scope of the embodiments should bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

1.-24. (canceled)
 25. A machine-readable medium that is not a transitorypropagating signal, the machine-readable medium include instructionsthat, when executed by a machine, cause the machine to performsoperations for monitoring physical interactions with merchandise, theoperations comprising: identifying a user; identifying an article basedon a physical relationship with the user; determining a user activitywith respect to the user and the article based on an observed context ofthe user and the article; and reporting the user activity.
 26. Themachine-readable medium of claim 25, wherein the physical relationshipincludes a distance between the article and the user.
 27. Themachine-readable medium of claim 25, wherein the physical relationshipincludes inclusion of both the user and the article within apredetermined geometric shape, the predetermined geometric shape beingone of a plurality of geometric shapes defined for a retail areaincluding the article.
 28. The machine-readable medium of claim 25,wherein the observed context includes article position information. 29.The method of claim 28, wherein the position information includes ageospatial position relative to a retail area including the article. 30.The machine-readable medium of claim 28, wherein the positioninformation includes an article arrangement.
 31. The machine-readablemedium of claim 30, wherein the article arrangement includes anorientation with respect to the user.
 32. The machine-readable medium ofclaim 25, wherein determining the user activity includes: tracking thearticle in a retail area; tracking the user in the retail area;identifying a point of interest in the retail area; and noting aconfluence of the article, the user, and the point of interest.
 33. Themachine-readable medium of claim 25, wherein reporting the user activityincludes communicating the activity to an incentives platform.
 34. Themachine-readable medium of claim 33, comprising delivering a purchaseincentive to the user from the incentives platform.
 35. Themachine-readable medium of claim 34, wherein the purchase incentiveincludes representations of other articles of interest to the user basedon a browsing history of the user.
 36. A system for monitoring physicalinteractions with merchandise, the system comprising: a user module toidentify a user; an activity module to: identify an article based on aphysical relationship with the user; and determine a user activity withrespect to the user and the article based on an observed context of theuser and the article; and a report module to report on the useractivity.
 37. The system of claim 36, wherein the physical relationshipincludes a distance between the article and the user.
 38. The system ofclaim 36, wherein the physical relationship includes inclusion of boththe user and the article within a predetermined geometric shape, thepredetermined geometric shape being one of a plurality of geometricshapes defined for a retail area including the article.
 39. The systemof claim 36, wherein the observed context includes article positioninformation.
 40. The system of claim 39, wherein the positioninformation includes a geospatial position relative to a retail areaincluding the article.
 41. The system of claim 39, wherein the positioninformation includes an article arrangement.
 42. The system of claim 41,wherein the article arrangement includes an orientation with respect tothe user.
 43. The system of claim 36, wherein to determine the useractivity includes: an article tracking module to track the article in aretail area; a user tracking module to track the user in the retailarea; a point-of-interest identification module to identify a point ofinterest in the retail area; and a correlation module to note aconfluence of the article, the user, and the point of interest.
 44. Thesystem of claim 36, wherein to report the user activity includescommunicating the activity to an incentives platform.
 45. The system ofclaim 44, comprising a delivery module to deliver a purchase incentiveto the user from the incentives platform.
 46. The system of claim 45,wherein the purchase incentive includes representations of otherarticles of interest to the user based on a browsing history of theuser.
 47. A hardware implemented method for monitoring physicalinteractions with merchandise, the method comprising: identifying auser; identifying an article based on a physical relationship with theuser; determining a user activity with respect to the user and thearticle based on an observed context of the user and the article; andreporting the user activity.
 48. The method of claim 47, wherein theuser activity includes physical possession of the article by the userand at least one of duration of possession or location of possession.